Overview

Dataset statistics

Number of variables38
Number of observations39322
Missing cells227433
Missing cells (%)15.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.9 MiB
Average record size in memory1.1 KiB

Variable types

Categorical19
DateTime2
Numeric17

Alerts

brand has a high cardinality: 329 distinct values High cardinality
model_code has a high cardinality: 38715 distinct values High cardinality
model_label has a high cardinality: 29558 distinct values High cardinality
commercial_label has a high cardinality: 4772 distinct values High cardinality
incorrect_fedas_code has a high cardinality: 2188 distinct values High cardinality
article_main_category has a high cardinality: 710 distinct values High cardinality
article_type has a high cardinality: 1221 distinct values High cardinality
article_detail has a high cardinality: 4076 distinct values High cardinality
comment has a high cardinality: 134 distinct values High cardinality
color_code has a high cardinality: 4399 distinct values High cardinality
color_label has a high cardinality: 12907 distinct values High cardinality
country_of_origin has a high cardinality: 74 distinct values High cardinality
country_of_manufacture has a high cardinality: 78 distinct values High cardinality
embakment_harbor has a high cardinality: 52 distinct values High cardinality
size has a high cardinality: 812 distinct values High cardinality
length is highly correlated with width and 1 other fieldsHigh correlation
width is highly correlated with length and 1 other fieldsHigh correlation
height is highly correlated with length and 1 other fieldsHigh correlation
minimum_multiple_of_order is highly correlated with net_weightHigh correlation
net_weight is highly correlated with minimum_multiple_of_order and 1 other fieldsHigh correlation
raw_weight is highly correlated with net_weightHigh correlation
correct_fedas_1 is highly correlated with incorrect_fedas_1High correlation
incorrect_fedas_1 is highly correlated with correct_fedas_1 and 3 other fieldsHigh correlation
incorrect_fedas_2 is highly correlated with incorrect_fedas_1 and 2 other fieldsHigh correlation
incorrect_fedas_3 is highly correlated with incorrect_fedas_1 and 2 other fieldsHigh correlation
incorrect_fedas_4 is highly correlated with incorrect_fedas_1 and 2 other fieldsHigh correlation
length is highly correlated with width and 1 other fieldsHigh correlation
width is highly correlated with lengthHigh correlation
height is highly correlated with lengthHigh correlation
net_weight is highly correlated with raw_weightHigh correlation
raw_weight is highly correlated with net_weightHigh correlation
incorrect_fedas_1 is highly correlated with incorrect_fedas_2 and 1 other fieldsHigh correlation
incorrect_fedas_2 is highly correlated with incorrect_fedas_1High correlation
incorrect_fedas_4 is highly correlated with incorrect_fedas_1High correlation
length is highly correlated with width and 1 other fieldsHigh correlation
width is highly correlated with length and 1 other fieldsHigh correlation
height is highly correlated with length and 1 other fieldsHigh correlation
net_weight is highly correlated with raw_weightHigh correlation
raw_weight is highly correlated with net_weightHigh correlation
correct_fedas_1 is highly correlated with incorrect_fedas_1High correlation
incorrect_fedas_1 is highly correlated with correct_fedas_1 and 1 other fieldsHigh correlation
incorrect_fedas_2 is highly correlated with incorrect_fedas_1High correlation
embakment_harbor is highly correlated with country_of_manufacture and 3 other fieldsHigh correlation
inaccurate_gender is highly correlated with accurate_genderHigh correlation
country_of_manufacture is highly correlated with embakment_harbor and 1 other fieldsHigh correlation
eco_furniture is highly correlated with embakment_harborHigh correlation
country_of_origin is highly correlated with embakment_harbor and 1 other fieldsHigh correlation
accurate_gender is highly correlated with inaccurate_gender and 1 other fieldsHigh correlation
correct_fedas_1 is highly correlated with embakment_harbor and 1 other fieldsHigh correlation
length is highly correlated with width and 3 other fieldsHigh correlation
width is highly correlated with length and 4 other fieldsHigh correlation
height is highly correlated with length and 3 other fieldsHigh correlation
inaccurate_gender is highly correlated with country_of_origin and 6 other fieldsHigh correlation
country_of_origin is highly correlated with width and 13 other fieldsHigh correlation
country_of_manufacture is highly correlated with width and 12 other fieldsHigh correlation
embakment_harbor is highly correlated with country_of_origin and 11 other fieldsHigh correlation
shipping_date is highly correlated with length and 6 other fieldsHigh correlation
eco_participation is highly correlated with eco_furnitureHigh correlation
eco_furniture is highly correlated with eco_participationHigh correlation
net_weight is highly correlated with raw_weightHigh correlation
raw_weight is highly correlated with net_weightHigh correlation
volume is highly correlated with length and 4 other fieldsHigh correlation
accurate_gender is highly correlated with inaccurate_gender and 7 other fieldsHigh correlation
correct_fedas_1 is highly correlated with inaccurate_gender and 6 other fieldsHigh correlation
correct_fedas_2 is highly correlated with country_of_origin and 5 other fieldsHigh correlation
correct_fedas_3 is highly correlated with country_of_origin and 6 other fieldsHigh correlation
correct_fedas_4 is highly correlated with inaccurate_gender and 4 other fieldsHigh correlation
incorrect_fedas_1 is highly correlated with inaccurate_gender and 8 other fieldsHigh correlation
incorrect_fedas_2 is highly correlated with country_of_origin and 7 other fieldsHigh correlation
incorrect_fedas_3 is highly correlated with country_of_origin and 7 other fieldsHigh correlation
incorrect_fedas_4 is highly correlated with inaccurate_gender and 8 other fieldsHigh correlation
commercial_label has 33084 (84.1%) missing values Missing
article_main_category has 751 (1.9%) missing values Missing
article_type has 920 (2.3%) missing values Missing
article_detail has 9700 (24.7%) missing values Missing
comment has 37770 (96.1%) missing values Missing
avalability_start_date has 14414 (36.7%) missing values Missing
avalability_end_date has 17318 (44.0%) missing values Missing
color_code has 12645 (32.2%) missing values Missing
inaccurate_gender has 19630 (49.9%) missing values Missing
country_of_origin has 14402 (36.6%) missing values Missing
country_of_manufacture has 14402 (36.6%) missing values Missing
embakment_harbor has 36549 (92.9%) missing values Missing
shipping_date has 15834 (40.3%) missing values Missing
length is highly skewed (γ1 = 55.40474886) Skewed
width is highly skewed (γ1 = 88.89368975) Skewed
height is highly skewed (γ1 = 32.8271002) Skewed
eco_participation is highly skewed (γ1 = 22.62168) Skewed
minimum_multiple_of_order is highly skewed (γ1 = 23.04697745) Skewed
net_weight is highly skewed (γ1 = 77.07187952) Skewed
raw_weight is highly skewed (γ1 = 108.8920108) Skewed
volume is highly skewed (γ1 = 34.39569575) Skewed
model_code is uniformly distributed Uniform
commercial_label is uniformly distributed Uniform
length has 38334 (97.5%) zeros Zeros
width has 38235 (97.2%) zeros Zeros
height has 38279 (97.3%) zeros Zeros
eco_participation has 38117 (96.9%) zeros Zeros
multiple_of_order has 7440 (18.9%) zeros Zeros
minimum_multiple_of_order has 17829 (45.3%) zeros Zeros
net_weight has 26860 (68.3%) zeros Zeros
raw_weight has 30149 (76.7%) zeros Zeros
volume has 34170 (86.9%) zeros Zeros
correct_fedas_2 has 7602 (19.3%) zeros Zeros
correct_fedas_4 has 1309 (3.3%) zeros Zeros
incorrect_fedas_2 has 3649 (9.3%) zeros Zeros
incorrect_fedas_4 has 987 (2.5%) zeros Zeros

Reproduction

Analysis started2022-11-11 11:55:55.555200
Analysis finished2022-11-11 11:56:28.660920
Duration33.11 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

brand
Categorical

HIGH CARDINALITY

Distinct329
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
brand_1
6089 
brand_293
 
1915
brand_383
 
1737
brand_56
 
1064
brand_243
 
1028
Other values (324)
27489 

Length

Max length9
Median length9
Mean length8.495371548
Min length7

Characters and Unicode

Total characters334055
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.1%

Sample

1st rowbrand_293
2nd rowbrand_3
3rd rowbrand_265
4th rowbrand_1
5th rowbrand_12

Common Values

ValueCountFrequency (%)
brand_16089
 
15.5%
brand_2931915
 
4.9%
brand_3831737
 
4.4%
brand_561064
 
2.7%
brand_2431028
 
2.6%
brand_102934
 
2.4%
brand_194919
 
2.3%
brand_285692
 
1.8%
brand_288681
 
1.7%
brand_175665
 
1.7%
Other values (319)23598
60.0%

Length

2022-11-11T12:56:28.704454image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brand_16089
 
15.5%
brand_2931915
 
4.9%
brand_3831737
 
4.4%
brand_561064
 
2.7%
brand_2431028
 
2.6%
brand_102934
 
2.4%
brand_194919
 
2.3%
brand_285692
 
1.8%
brand_288681
 
1.7%
brand_175665
 
1.7%
Other values (319)23598
60.0%

Most occurring characters

ValueCountFrequency (%)
b39322
11.8%
r39322
11.8%
a39322
11.8%
n39322
11.8%
d39322
11.8%
_39322
11.8%
119580
5.9%
318242
5.5%
213777
 
4.1%
97782
 
2.3%
Other values (6)38742
11.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter196610
58.9%
Decimal Number98123
29.4%
Connector Punctuation39322
 
11.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
119580
20.0%
318242
18.6%
213777
14.0%
97782
 
7.9%
87686
 
7.8%
47685
 
7.8%
57527
 
7.7%
76126
 
6.2%
04871
 
5.0%
64847
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
b39322
20.0%
r39322
20.0%
a39322
20.0%
n39322
20.0%
d39322
20.0%
Connector Punctuation
ValueCountFrequency (%)
_39322
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin196610
58.9%
Common137445
41.1%

Most frequent character per script

Common
ValueCountFrequency (%)
_39322
28.6%
119580
14.2%
318242
13.3%
213777
 
10.0%
97782
 
5.7%
87686
 
5.6%
47685
 
5.6%
57527
 
5.5%
76126
 
4.5%
04871
 
3.5%
Latin
ValueCountFrequency (%)
b39322
20.0%
r39322
20.0%
a39322
20.0%
n39322
20.0%
d39322
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII334055
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b39322
11.8%
r39322
11.8%
a39322
11.8%
n39322
11.8%
d39322
11.8%
_39322
11.8%
119580
5.9%
318242
5.5%
213777
 
4.1%
97782
 
2.3%
Other values (6)38742
11.6%

model_code
Categorical

HIGH CARDINALITY
UNIFORM

Distinct38715
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
813271-40
 
4
1865971
 
3
0494394
 
3
813800-40
 
3
214304
 
3
Other values (38710)
39306 

Length

Max length21
Median length18
Mean length7.689893698
Min length2

Characters and Unicode

Total characters302382
Distinct characters43
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38133 ?
Unique (%)97.0%

Sample

1st rowS42783
2nd rowR1252
3rd rowOXS917808
4th rowGM5253
5th rowMS338

Common Values

ValueCountFrequency (%)
813271-404
 
< 0.1%
18659713
 
< 0.1%
04943943
 
< 0.1%
813800-403
 
< 0.1%
2143043
 
< 0.1%
813750-403
 
< 0.1%
813721-403
 
< 0.1%
813320-403
 
< 0.1%
10213
 
< 0.1%
813180-403
 
< 0.1%
Other values (38705)39291
99.9%

Length

2022-11-11T12:56:28.793989image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
int13
 
< 0.1%
j8
 
< 0.1%
15
 
< 0.1%
813271-404
 
< 0.1%
e4
 
< 0.1%
serraline3
 
< 0.1%
pro3
 
< 0.1%
kt3
 
< 0.1%
813380-403
 
< 0.1%
ridge3
 
< 0.1%
Other values (38722)39332
99.9%

Most occurring characters

ValueCountFrequency (%)
045054
14.9%
132947
10.9%
223988
 
7.9%
320221
 
6.7%
518073
 
6.0%
617491
 
5.8%
416861
 
5.6%
816676
 
5.5%
715812
 
5.2%
914392
 
4.8%
Other values (33)80867
26.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number221515
73.3%
Uppercase Letter75116
 
24.8%
Dash Punctuation3752
 
1.2%
Other Punctuation1915
 
0.6%
Space Separator62
 
< 0.1%
Connector Punctuation20
 
< 0.1%
Lowercase Letter2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F6092
 
8.1%
A5128
 
6.8%
G4833
 
6.4%
W4535
 
6.0%
E4053
 
5.4%
D3987
 
5.3%
B3667
 
4.9%
M3626
 
4.8%
S3424
 
4.6%
I3159
 
4.2%
Other values (16)32612
43.4%
Decimal Number
ValueCountFrequency (%)
045054
20.3%
132947
14.9%
223988
10.8%
320221
9.1%
518073
8.2%
617491
 
7.9%
416861
 
7.6%
816676
 
7.5%
715812
 
7.1%
914392
 
6.5%
Other Punctuation
ValueCountFrequency (%)
.1410
73.6%
/505
 
26.4%
Lowercase Letter
ValueCountFrequency (%)
p1
50.0%
l1
50.0%
Dash Punctuation
ValueCountFrequency (%)
-3752
100.0%
Space Separator
ValueCountFrequency (%)
62
100.0%
Connector Punctuation
ValueCountFrequency (%)
_20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common227264
75.2%
Latin75118
 
24.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
F6092
 
8.1%
A5128
 
6.8%
G4833
 
6.4%
W4535
 
6.0%
E4053
 
5.4%
D3987
 
5.3%
B3667
 
4.9%
M3626
 
4.8%
S3424
 
4.6%
I3159
 
4.2%
Other values (18)32614
43.4%
Common
ValueCountFrequency (%)
045054
19.8%
132947
14.5%
223988
10.6%
320221
8.9%
518073
8.0%
617491
 
7.7%
416861
 
7.4%
816676
 
7.3%
715812
 
7.0%
914392
 
6.3%
Other values (5)5749
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII302382
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
045054
14.9%
132947
10.9%
223988
 
7.9%
320221
 
6.7%
518073
 
6.0%
617491
 
5.8%
416861
 
5.6%
816676
 
5.5%
715812
 
5.2%
914392
 
4.8%
Other values (33)80867
26.7%

model_label
Categorical

HIGH CARDINALITY

Distinct29558
Distinct (%)75.2%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
MAN JEANS
 
86
VESTE
 
77
WOMAN JEANS
 
64
CREWNECK T-SHIRT
 
61
CHUCK TAYLOR ALL STAR
 
47
Other values (29553)
38987 

Length

Max length24518
Median length26
Mean length18.81748131
Min length1

Characters and Unicode

Total characters739941
Distinct characters104
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25415 ?
Unique (%)64.6%

Sample

1st rowFLEXAGON ENERGY TR 3.0 MT
2nd rowTADEN PLUS FUR
3rd rowPOCHETTE PORTE TRAVERS PE
4th rowCLUB KNOT TANK
5th rowBONITA DK PNK/BLCK M

Common Values

ValueCountFrequency (%)
MAN JEANS86
 
0.2%
VESTE77
 
0.2%
WOMAN JEANS64
 
0.2%
CREWNECK T-SHIRT61
 
0.2%
CHUCK TAYLOR ALL STAR47
 
0.1%
CL LTHR40
 
0.1%
TEE39
 
0.1%
BIKINI35
 
0.1%
REEBOK ROYAL GLIDE28
 
0.1%
REEBOK ROYAL GLIDE RPLCLP28
 
0.1%
Other values (29548)38817
98.7%

Length

2022-11-11T12:56:28.886346image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
w2278
 
1.7%
m2016
 
1.5%
tee1594
 
1.2%
jacket892
 
0.7%
j789
 
0.6%
ss763
 
0.6%
top750
 
0.5%
jr728
 
0.5%
2.0692
 
0.5%
short680
 
0.5%
Other values (18443)125990
91.8%

Most occurring characters

ValueCountFrequency (%)
98108
 
13.3%
E61815
 
8.4%
A46466
 
6.3%
T44884
 
6.1%
R41573
 
5.6%
S40283
 
5.4%
O40012
 
5.4%
I36243
 
4.9%
L34825
 
4.7%
N32549
 
4.4%
Other values (94)263183
35.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter591698
80.0%
Space Separator98135
 
13.3%
Decimal Number33941
 
4.6%
Other Punctuation10931
 
1.5%
Dash Punctuation3051
 
0.4%
Lowercase Letter534
 
0.1%
Math Symbol461
 
0.1%
Open Punctuation322
 
< 0.1%
Control309
 
< 0.1%
Close Punctuation269
 
< 0.1%
Other values (4)290
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E61815
 
10.4%
A46466
 
7.9%
T44884
 
7.6%
R41573
 
7.0%
S40283
 
6.8%
O40012
 
6.8%
I36243
 
6.1%
L34825
 
5.9%
N32549
 
5.5%
C26701
 
4.5%
Other values (27)186347
31.5%
Lowercase Letter
ValueCountFrequency (%)
m143
26.8%
h140
26.2%
l139
26.0%
s24
 
4.5%
o19
 
3.6%
e10
 
1.9%
r8
 
1.5%
c7
 
1.3%
a6
 
1.1%
t5
 
0.9%
Other values (14)33
 
6.2%
Other Punctuation
ValueCountFrequency (%)
;5510
50.4%
.2177
 
19.9%
/1978
 
18.1%
'763
 
7.0%
,167
 
1.5%
&138
 
1.3%
\85
 
0.8%
"51
 
0.5%
?24
 
0.2%
#13
 
0.1%
Other values (3)25
 
0.2%
Decimal Number
ValueCountFrequency (%)
08448
24.9%
25780
17.0%
15394
15.9%
34003
11.8%
52326
 
6.9%
41802
 
5.3%
91728
 
5.1%
61668
 
4.9%
71495
 
4.4%
81297
 
3.8%
Control
ValueCountFrequency (%)
145
46.9%
145
46.9%
™11
 
3.6%
ƒ5
 
1.6%
‚3
 
1.0%
Math Symbol
ValueCountFrequency (%)
+449
97.4%
>11
 
2.4%
|1
 
0.2%
Space Separator
ValueCountFrequency (%)
98108
> 99.9%
 27
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
(295
91.6%
[27
 
8.4%
Close Punctuation
ValueCountFrequency (%)
)242
90.0%
]27
 
10.0%
Other Symbol
ValueCountFrequency (%)
°206
91.2%
®20
 
8.8%
Dash Punctuation
ValueCountFrequency (%)
-3051
100.0%
Connector Punctuation
ValueCountFrequency (%)
_56
100.0%
Initial Punctuation
ValueCountFrequency (%)
«6
100.0%
Other Number
ValueCountFrequency (%)
²2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin592232
80.0%
Common147709
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E61815
 
10.4%
A46466
 
7.8%
T44884
 
7.6%
R41573
 
7.0%
S40283
 
6.8%
O40012
 
6.8%
I36243
 
6.1%
L34825
 
5.9%
N32549
 
5.5%
C26701
 
4.5%
Other values (51)186881
31.6%
Common
ValueCountFrequency (%)
98108
66.4%
08448
 
5.7%
25780
 
3.9%
;5510
 
3.7%
15394
 
3.7%
34003
 
2.7%
-3051
 
2.1%
52326
 
1.6%
.2177
 
1.5%
/1978
 
1.3%
Other values (33)10934
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII739598
> 99.9%
None343
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
98108
 
13.3%
E61815
 
8.4%
A46466
 
6.3%
T44884
 
6.1%
R41573
 
5.6%
S40283
 
5.4%
O40012
 
5.4%
I36243
 
4.9%
L34825
 
4.7%
N32549
 
4.4%
Other values (75)262840
35.5%
None
ValueCountFrequency (%)
°206
60.1%
É29
 
8.5%
 27
 
7.9%
®20
 
5.8%
Ã13
 
3.8%
™11
 
3.2%
«6
 
1.7%
À5
 
1.5%
ƒ5
 
1.5%
Â4
 
1.2%
Other values (9)17
 
5.0%

commercial_label
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct4772
Distinct (%)76.5%
Missing33084
Missing (%)84.1%
Memory size1.4 MiB
TBT_AP_MN TOP
 
20
GM500 D
 
15
SLENDER
 
13
PC574 M
 
13
PALM BEACH
 
13
Other values (4767)
6164 

Length

Max length25
Median length22
Mean length16.5801539
Min length1

Characters and Unicode

Total characters103427
Distinct characters61
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3926 ?
Unique (%)62.9%

Sample

1st rowOMEGAS 160 CAPSULES
2nd rowJORDAN LEGEND ANKLE 6PK
3rd rowSHORT DE MUAY THAI VENUM
4th rowFOOT T5 TRAINER FC BARCEL
5th rowPAGAIE KAYAK SYMETRIQUE,

Common Values

ValueCountFrequency (%)
TBT_AP_MN TOP20
 
0.1%
GM500 D15
 
< 0.1%
SLENDER13
 
< 0.1%
PC574 M13
 
< 0.1%
PALM BEACH13
 
< 0.1%
CLASH13
 
< 0.1%
YV574 M12
 
< 0.1%
POLO MANCHES COURTES12
 
< 0.1%
TEE12
 
< 0.1%
GC574 M11
 
< 0.1%
Other values (4762)6104
 
15.5%
(Missing)33084
84.1%

Length

2022-11-11T12:56:28.973963image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
icepeak525
 
2.9%
de333
 
1.8%
284
 
1.6%
m218
 
1.2%
luhta199
 
1.1%
jr192
 
1.0%
homme160
 
0.9%
d145
 
0.8%
tee135
 
0.7%
a131
 
0.7%
Other values (5137)15967
87.3%

Most occurring characters

ValueCountFrequency (%)
12301
 
11.9%
E10053
 
9.7%
A7295
 
7.1%
T6091
 
5.9%
S5457
 
5.3%
O5405
 
5.2%
R5110
 
4.9%
I5099
 
4.9%
L4706
 
4.6%
N4432
 
4.3%
Other values (51)37478
36.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter82662
79.9%
Space Separator12315
 
11.9%
Decimal Number7062
 
6.8%
Dash Punctuation617
 
0.6%
Other Punctuation562
 
0.5%
Math Symbol80
 
0.1%
Connector Punctuation60
 
0.1%
Open Punctuation28
 
< 0.1%
Lowercase Letter13
 
< 0.1%
Other Symbol13
 
< 0.1%
Other values (2)15
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E10053
12.2%
A7295
 
8.8%
T6091
 
7.4%
S5457
 
6.6%
O5405
 
6.5%
R5110
 
6.2%
I5099
 
6.2%
L4706
 
5.7%
N4432
 
5.4%
C4063
 
4.9%
Other values (19)24951
30.2%
Decimal Number
ValueCountFrequency (%)
01715
24.3%
31096
15.5%
11075
15.2%
2845
12.0%
5628
 
8.9%
9379
 
5.4%
4378
 
5.4%
7362
 
5.1%
6299
 
4.2%
8285
 
4.0%
Other Punctuation
ValueCountFrequency (%)
/252
44.8%
.131
23.3%
,93
 
16.5%
'65
 
11.6%
&14
 
2.5%
"5
 
0.9%
%2
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
o10
76.9%
u1
 
7.7%
r1
 
7.7%
f1
 
7.7%
Space Separator
ValueCountFrequency (%)
12301
99.9%
 14
 
0.1%
Math Symbol
ValueCountFrequency (%)
+63
78.8%
>17
 
21.2%
Other Symbol
ValueCountFrequency (%)
°8
61.5%
®5
38.5%
Dash Punctuation
ValueCountFrequency (%)
-617
100.0%
Connector Punctuation
ValueCountFrequency (%)
_60
100.0%
Open Punctuation
ValueCountFrequency (%)
(28
100.0%
Close Punctuation
ValueCountFrequency (%)
)10
100.0%
Control
ValueCountFrequency (%)
ƒ5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin82675
79.9%
Common20752
 
20.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E10053
12.2%
A7295
 
8.8%
T6091
 
7.4%
S5457
 
6.6%
O5405
 
6.5%
R5110
 
6.2%
I5099
 
6.2%
L4706
 
5.7%
N4432
 
5.4%
C4063
 
4.9%
Other values (23)24964
30.2%
Common
ValueCountFrequency (%)
12301
59.3%
01715
 
8.3%
31096
 
5.3%
11075
 
5.2%
2845
 
4.1%
5628
 
3.0%
-617
 
3.0%
9379
 
1.8%
4378
 
1.8%
7362
 
1.7%
Other values (18)1356
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII103391
> 99.9%
None36
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12301
 
11.9%
E10053
 
9.7%
A7295
 
7.1%
T6091
 
5.9%
S5457
 
5.3%
O5405
 
5.2%
R5110
 
4.9%
I5099
 
4.9%
L4706
 
4.6%
N4432
 
4.3%
Other values (44)37442
36.2%
None
ValueCountFrequency (%)
 14
38.9%
°8
22.2%
®5
 
13.9%
ƒ5
 
13.9%
É2
 
5.6%
À1
 
2.8%
È1
 
2.8%

incorrect_fedas_code
Categorical

HIGH CARDINALITY

Distinct2188
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
10854 
275124
 
467
375311
 
418
375313
 
393
278125
 
307
Other values (2183)
26883 

Length

Max length6
Median length6
Mean length4.343827883
Min length0

Characters and Unicode

Total characters170808
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique550 ?
Unique (%)1.4%

Sample

1st row378011
2nd row
3rd row175897
4th row224122
5th row

Common Values

ValueCountFrequency (%)
10854
27.6%
275124467
 
1.2%
375311418
 
1.1%
375313393
 
1.0%
278125307
 
0.8%
232377288
 
0.7%
375312282
 
0.7%
275121258
 
0.7%
275125256
 
0.7%
232904253
 
0.6%
Other values (2178)25546
65.0%

Length

2022-11-11T12:56:29.046589image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
275124467
 
1.6%
375311418
 
1.5%
375313393
 
1.4%
278125307
 
1.1%
232377288
 
1.0%
375312282
 
1.0%
275121258
 
0.9%
275125256
 
0.9%
232904253
 
0.9%
375963243
 
0.9%
Other values (2177)25303
88.9%

Most occurring characters

ValueCountFrequency (%)
231425
18.4%
123437
13.7%
321305
12.5%
720212
11.8%
017128
10.0%
516053
9.4%
412109
 
7.1%
910156
 
5.9%
89848
 
5.8%
69135
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number170808
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
231425
18.4%
123437
13.7%
321305
12.5%
720212
11.8%
017128
10.0%
516053
9.4%
412109
 
7.1%
910156
 
5.9%
89848
 
5.8%
69135
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common170808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
231425
18.4%
123437
13.7%
321305
12.5%
720212
11.8%
017128
10.0%
516053
9.4%
412109
 
7.1%
910156
 
5.9%
89848
 
5.8%
69135
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII170808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
231425
18.4%
123437
13.7%
321305
12.5%
720212
11.8%
017128
10.0%
516053
9.4%
412109
 
7.1%
910156
 
5.9%
89848
 
5.8%
69135
 
5.3%

article_main_category
Categorical

HIGH CARDINALITY
MISSING

Distinct710
Distinct (%)1.8%
Missing751
Missing (%)1.9%
Memory size2.5 MiB
LOISIRS
2855 
FOOTBALL
 
2518
TRAINING
 
2498
SPORTSTYLE
 
2310
LOISIR
 
1770
Other values (705)
26620 

Length

Max length35
Median length33
Mean length10.01521869
Min length2

Characters and Unicode

Total characters386297
Distinct characters50
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique145 ?
Unique (%)0.4%

Sample

1st rowTRAINING
2nd rowGARDEN
3rd rowSAC
4th rowRACKET SPORTS
5th rowTARTAN CHECKS

Common Values

ValueCountFrequency (%)
LOISIRS2855
 
7.3%
FOOTBALL2518
 
6.4%
TRAINING2498
 
6.4%
SPORTSTYLE2310
 
5.9%
LOISIR1770
 
4.5%
RUNNING1263
 
3.2%
APPAREL1063
 
2.7%
OUTDOOR945
 
2.4%
MULTISPORT845
 
2.1%
COLLECTIVITES748
 
1.9%
Other values (700)21756
55.3%
(Missing)751
 
1.9%

Length

2022-11-11T12:56:29.123269image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
loisirs3132
 
6.1%
football2563
 
5.0%
training2549
 
4.9%
sports2384
 
4.6%
sportstyle2310
 
4.5%
loisir1906
 
3.7%
textile1613
 
3.1%
running1441
 
2.8%
apparel1428
 
2.8%
outdoor1388
 
2.7%
Other values (638)30792
59.8%

Most occurring characters

ValueCountFrequency (%)
S39455
10.2%
I35023
 
9.1%
E34986
 
9.1%
O34666
 
9.0%
T34137
 
8.8%
R32467
 
8.4%
L27171
 
7.0%
A23171
 
6.0%
N20988
 
5.4%
13282
 
3.4%
Other values (40)90951
23.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter370373
95.9%
Space Separator13282
 
3.4%
Other Punctuation2102
 
0.5%
Dash Punctuation371
 
0.1%
Decimal Number124
 
< 0.1%
Math Symbol26
 
< 0.1%
Lowercase Letter7
 
< 0.1%
Open Punctuation6
 
< 0.1%
Close Punctuation6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S39455
10.7%
I35023
9.5%
E34986
9.4%
O34666
9.4%
T34137
9.2%
R32467
8.8%
L27171
 
7.3%
A23171
 
6.3%
N20988
 
5.7%
P12526
 
3.4%
Other values (17)75783
20.5%
Decimal Number
ValueCountFrequency (%)
168
54.8%
227
 
21.8%
318
 
14.5%
83
 
2.4%
63
 
2.4%
53
 
2.4%
42
 
1.6%
Other Punctuation
ValueCountFrequency (%)
/1562
74.3%
&361
 
17.2%
.66
 
3.1%
,42
 
2.0%
?36
 
1.7%
'35
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
i2
28.6%
l2
28.6%
r1
14.3%
e1
14.3%
s1
14.3%
Space Separator
ValueCountFrequency (%)
13282
100.0%
Dash Punctuation
ValueCountFrequency (%)
-371
100.0%
Math Symbol
ValueCountFrequency (%)
+26
100.0%
Open Punctuation
ValueCountFrequency (%)
(6
100.0%
Close Punctuation
ValueCountFrequency (%)
)6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin370380
95.9%
Common15917
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S39455
10.7%
I35023
9.5%
E34986
9.4%
O34666
9.4%
T34137
9.2%
R32467
8.8%
L27171
 
7.3%
A23171
 
6.3%
N20988
 
5.7%
P12526
 
3.4%
Other values (22)75790
20.5%
Common
ValueCountFrequency (%)
13282
83.4%
/1562
 
9.8%
-371
 
2.3%
&361
 
2.3%
168
 
0.4%
.66
 
0.4%
,42
 
0.3%
?36
 
0.2%
'35
 
0.2%
227
 
0.2%
Other values (8)67
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII386261
> 99.9%
None36
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S39455
10.2%
I35023
 
9.1%
E34986
 
9.1%
O34666
 
9.0%
T34137
 
8.8%
R32467
 
8.4%
L27171
 
7.0%
A23171
 
6.0%
N20988
 
5.4%
13282
 
3.4%
Other values (39)90915
23.5%
None
ValueCountFrequency (%)
Ã36
100.0%

article_type
Categorical

HIGH CARDINALITY
MISSING

Distinct1221
Distinct (%)3.2%
Missing920
Missing (%)2.3%
Memory size2.5 MiB
HOMME
3906 
FEMME
3470 
UNISEXE ADULTE
 
1471
SHOES - LOW (NON FOOTBALL)
 
851
MEN
 
845
Other values (1216)
27859 

Length

Max length34
Median length31
Mean length9.292693089
Min length1

Characters and Unicode

Total characters356858
Distinct characters63
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique317 ?
Unique (%)0.8%

Sample

1st rowHOMME
2nd rowRUBBER BOOTS
3rd rowHOMME
4th rowFEMME
5th rowMEN

Common Values

ValueCountFrequency (%)
HOMME3906
 
9.9%
FEMME3470
 
8.8%
UNISEXE ADULTE1471
 
3.7%
SHOES - LOW (NON FOOTBALL)851
 
2.2%
MEN845
 
2.1%
GARCON764
 
1.9%
VESTE721
 
1.8%
WOMEN642
 
1.6%
UNISEXE ENFANT580
 
1.5%
UNISEX572
 
1.5%
Other values (1211)24580
62.5%
(Missing)920
 
2.3%

Length

2022-11-11T12:56:29.211250image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
homme3915
 
6.8%
femme3473
 
6.0%
unisexe2262
 
3.9%
adulte1481
 
2.6%
shoes1275
 
2.2%
unisex1067
 
1.9%
1028
 
1.8%
men1000
 
1.7%
football948
 
1.6%
veste880
 
1.5%
Other values (1095)40290
69.9%

Most occurring characters

ValueCountFrequency (%)
E52712
14.8%
S33070
 
9.3%
T26057
 
7.3%
A22992
 
6.4%
O22781
 
6.4%
M22543
 
6.3%
N21520
 
6.0%
19217
 
5.4%
R15557
 
4.4%
L14477
 
4.1%
Other values (53)105932
29.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter327898
91.9%
Space Separator19217
 
5.4%
Dash Punctuation2450
 
0.7%
Open Punctuation2017
 
0.6%
Close Punctuation1884
 
0.5%
Other Punctuation1770
 
0.5%
Decimal Number1523
 
0.4%
Lowercase Letter92
 
< 0.1%
Math Symbol4
 
< 0.1%
Modifier Symbol2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E52712
16.1%
S33070
10.1%
T26057
 
7.9%
A22992
 
7.0%
O22781
 
6.9%
M22543
 
6.9%
N21520
 
6.6%
R15557
 
4.7%
L14477
 
4.4%
I14215
 
4.3%
Other values (17)81974
25.0%
Lowercase Letter
ValueCountFrequency (%)
u14
15.2%
r12
13.0%
a11
12.0%
d10
10.9%
e10
10.9%
o10
10.9%
b6
6.5%
é6
6.5%
n6
6.5%
c3
 
3.3%
Other values (4)4
 
4.3%
Decimal Number
ValueCountFrequency (%)
1639
42.0%
0309
20.3%
5257
16.9%
2118
 
7.7%
991
 
6.0%
443
 
2.8%
339
 
2.6%
620
 
1.3%
75
 
0.3%
82
 
0.1%
Other Punctuation
ValueCountFrequency (%)
/1527
86.3%
&113
 
6.4%
'106
 
6.0%
,22
 
1.2%
.2
 
0.1%
Space Separator
ValueCountFrequency (%)
19217
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2450
100.0%
Open Punctuation
ValueCountFrequency (%)
(2017
100.0%
Close Punctuation
ValueCountFrequency (%)
)1884
100.0%
Math Symbol
ValueCountFrequency (%)
+4
100.0%
Modifier Symbol
ValueCountFrequency (%)
´2
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin327990
91.9%
Common28868
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E52712
16.1%
S33070
10.1%
T26057
 
7.9%
A22992
 
7.0%
O22781
 
6.9%
M22543
 
6.9%
N21520
 
6.6%
R15557
 
4.7%
L14477
 
4.4%
I14215
 
4.3%
Other values (31)82066
25.0%
Common
ValueCountFrequency (%)
19217
66.6%
-2450
 
8.5%
(2017
 
7.0%
)1884
 
6.5%
/1527
 
5.3%
1639
 
2.2%
0309
 
1.1%
5257
 
0.9%
2118
 
0.4%
&113
 
0.4%
Other values (12)337
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII356847
> 99.9%
None11
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E52712
14.8%
S33070
 
9.3%
T26057
 
7.3%
A22992
 
6.4%
O22781
 
6.4%
M22543
 
6.3%
N21520
 
6.0%
19217
 
5.4%
R15557
 
4.4%
L14477
 
4.1%
Other values (49)105921
29.7%
None
ValueCountFrequency (%)
é6
54.5%
Ê2
 
18.2%
´2
 
18.2%
1
 
9.1%

article_detail
Categorical

HIGH CARDINALITY
MISSING

Distinct4076
Distinct (%)13.8%
Missing9700
Missing (%)24.7%
Memory size2.2 MiB
09-SHOES (LOW)
 
1436
ADULT MALE
 
1125
ADULT FEMALE
 
950
30-JERSEY
 
339
ADULT UNISEX
 
321
Other values (4071)
25451 

Length

Max length35
Median length29
Mean length11.53443387
Min length1

Characters and Unicode

Total characters341673
Distinct characters75
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2606 ?
Unique (%)8.8%

Sample

1st row09-SHOES (LOW)
2nd rowBOOTS
3rd rowN1FARROW
4th row21-TANK
5th rowT-SHIRT

Common Values

ValueCountFrequency (%)
09-SHOES (LOW)1436
 
3.7%
ADULT MALE1125
 
2.9%
ADULT FEMALE950
 
2.4%
30-JERSEY339
 
0.9%
ADULT UNISEX321
 
0.8%
MANCHE LONGUE306
 
0.8%
TEE SHIRT MC279
 
0.7%
DENIM PANTS276
 
0.7%
44-PANTS (1/1)266
 
0.7%
PANTALON242
 
0.6%
Other values (4066)24082
61.2%
(Missing)9700
24.7%

Length

2022-11-11T12:56:29.299960image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
adult2396
 
4.4%
low1695
 
3.1%
09-shoes1436
 
2.6%
male1352
 
2.5%
female1063
 
1.9%
de757
 
1.4%
kids733
 
1.3%
short724
 
1.3%
unisex680
 
1.2%
top662
 
1.2%
Other values (3557)43015
78.9%

Most occurring characters

ValueCountFrequency (%)
E35249
 
10.3%
S28516
 
8.3%
T25151
 
7.4%
25121
 
7.4%
A24031
 
7.0%
O20044
 
5.9%
L19072
 
5.6%
R16000
 
4.7%
I15638
 
4.6%
N13097
 
3.8%
Other values (65)119754
35.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter286481
83.8%
Space Separator25124
 
7.4%
Decimal Number15849
 
4.6%
Dash Punctuation6317
 
1.8%
Open Punctuation2223
 
0.7%
Close Punctuation2221
 
0.7%
Other Punctuation2120
 
0.6%
Lowercase Letter1248
 
0.4%
Math Symbol63
 
< 0.1%
Modifier Symbol17
 
< 0.1%
Other values (2)10
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E35249
12.3%
S28516
 
10.0%
T25151
 
8.8%
A24031
 
8.4%
O20044
 
7.0%
L19072
 
6.7%
R16000
 
5.6%
I15638
 
5.5%
N13097
 
4.6%
C10597
 
3.7%
Other values (20)79086
27.6%
Lowercase Letter
ValueCountFrequency (%)
o155
12.4%
a145
11.6%
s134
10.7%
l127
10.2%
p93
7.5%
r91
7.3%
e88
 
7.1%
d72
 
5.8%
y59
 
4.7%
b58
 
4.6%
Other values (8)226
18.1%
Decimal Number
ValueCountFrequency (%)
03383
21.3%
12646
16.7%
91927
12.2%
31814
11.4%
41811
11.4%
21555
9.8%
51043
 
6.6%
7775
 
4.9%
6471
 
3.0%
8424
 
2.7%
Other Punctuation
ValueCountFrequency (%)
/1502
70.8%
%173
 
8.2%
.141
 
6.7%
,126
 
5.9%
&116
 
5.5%
'49
 
2.3%
"7
 
0.3%
?6
 
0.3%
Space Separator
ValueCountFrequency (%)
25121
> 99.9%
 3
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-6317
100.0%
Open Punctuation
ValueCountFrequency (%)
(2223
100.0%
Close Punctuation
ValueCountFrequency (%)
)2221
100.0%
Math Symbol
ValueCountFrequency (%)
+63
100.0%
Modifier Symbol
ValueCountFrequency (%)
´17
100.0%
Other Symbol
ValueCountFrequency (%)
®9
100.0%
Control
ValueCountFrequency (%)
’1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin287729
84.2%
Common53944
 
15.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
E35249
12.3%
S28516
 
9.9%
T25151
 
8.7%
A24031
 
8.4%
O20044
 
7.0%
L19072
 
6.6%
R16000
 
5.6%
I15638
 
5.4%
N13097
 
4.6%
C10597
 
3.7%
Other values (38)80334
27.9%
Common
ValueCountFrequency (%)
25121
46.6%
-6317
 
11.7%
03383
 
6.3%
12646
 
4.9%
(2223
 
4.1%
)2221
 
4.1%
91927
 
3.6%
31814
 
3.4%
41811
 
3.4%
21555
 
2.9%
Other values (17)4926
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII341623
> 99.9%
None50
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E35249
 
10.3%
S28516
 
8.3%
T25151
 
7.4%
25121
 
7.4%
A24031
 
7.0%
O20044
 
5.9%
L19072
 
5.6%
R16000
 
4.7%
I15638
 
4.6%
N13097
 
3.8%
Other values (57)119704
35.0%
None
ValueCountFrequency (%)
´17
34.0%
É14
28.0%
®9
18.0%
 3
 
6.0%
Ã3
 
6.0%
Ê2
 
4.0%
Î1
 
2.0%
’1
 
2.0%

comment
Categorical

HIGH CARDINALITY
MISSING

Distinct134
Distinct (%)8.6%
Missing37770
Missing (%)96.1%
Memory size1.2 MiB
VETEMENT
413 
SWI
131 
SNO
 
79
T-SHIRT
 
69
MATERIEL RANDONNEE
 
50
Other values (129)
810 

Length

Max length33
Median length28
Mean length7.914948454
Min length2

Characters and Unicode

Total characters12284
Distinct characters61
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)3.7%

Sample

1st rowMATERIEL RANDONNEE
2nd rowSNO
3rd rowMID CUT DETENTE
4th rowJUNIOR
5th rowFERMANT

Common Values

ValueCountFrequency (%)
VETEMENT413
 
1.1%
SWI131
 
0.3%
SNO79
 
0.2%
T-SHIRT69
 
0.2%
MATERIEL RANDONNEE50
 
0.1%
SAC A DOS47
 
0.1%
FERMANT47
 
0.1%
MANCHES COURTES35
 
0.1%
CHAUSSURE34
 
0.1%
ACCESSOIRES34
 
0.1%
Other values (124)613
 
1.6%
(Missing)37770
96.1%

Length

2022-11-11T12:56:29.386364image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vetement413
20.8%
swi131
 
6.6%
sno79
 
4.0%
t-shirt76
 
3.8%
manches69
 
3.5%
sac67
 
3.4%
materiel50
 
2.5%
randonnee50
 
2.5%
a48
 
2.4%
dos47
 
2.4%
Other values (166)960
48.2%

Most occurring characters

ValueCountFrequency (%)
E2206
18.0%
T1419
11.6%
N996
 
8.1%
S965
 
7.9%
M704
 
5.7%
A697
 
5.7%
R518
 
4.2%
C499
 
4.1%
V486
 
4.0%
I485
 
3.9%
Other values (51)3309
26.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter11326
92.2%
Space Separator438
 
3.6%
Decimal Number267
 
2.2%
Lowercase Letter126
 
1.0%
Dash Punctuation110
 
0.9%
Other Punctuation14
 
0.1%
Math Symbol1
 
< 0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E2206
19.5%
T1419
12.5%
N996
8.8%
S965
8.5%
M704
 
6.2%
A697
 
6.2%
R518
 
4.6%
C499
 
4.4%
V486
 
4.3%
I485
 
4.3%
Other values (16)2351
20.8%
Lowercase Letter
ValueCountFrequency (%)
r14
11.1%
i13
10.3%
t13
10.3%
e12
9.5%
u11
8.7%
a8
 
6.3%
h8
 
6.3%
n7
 
5.6%
c7
 
5.6%
p6
 
4.8%
Other values (9)27
21.4%
Decimal Number
ValueCountFrequency (%)
166
24.7%
034
12.7%
432
12.0%
829
10.9%
228
10.5%
627
10.1%
924
 
9.0%
318
 
6.7%
58
 
3.0%
71
 
0.4%
Space Separator
ValueCountFrequency (%)
438
100.0%
Dash Punctuation
ValueCountFrequency (%)
-110
100.0%
Other Punctuation
ValueCountFrequency (%)
/14
100.0%
Math Symbol
ValueCountFrequency (%)
+1
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11452
93.2%
Common832
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
E2206
19.3%
T1419
12.4%
N996
8.7%
S965
 
8.4%
M704
 
6.1%
A697
 
6.1%
R518
 
4.5%
C499
 
4.4%
V486
 
4.2%
I485
 
4.2%
Other values (35)2477
21.6%
Common
ValueCountFrequency (%)
438
52.6%
-110
 
13.2%
166
 
7.9%
034
 
4.1%
432
 
3.8%
829
 
3.5%
228
 
3.4%
627
 
3.2%
924
 
2.9%
318
 
2.2%
Other values (6)26
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII12284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E2206
18.0%
T1419
11.6%
N996
 
8.1%
S965
 
7.9%
M704
 
5.7%
A697
 
5.7%
R518
 
4.2%
C499
 
4.1%
V486
 
4.0%
I485
 
3.9%
Other values (51)3309
26.9%
Distinct263
Distinct (%)1.1%
Missing14414
Missing (%)36.7%
Memory size307.3 KiB
Minimum2000-01-01 00:00:00
Maximum2021-05-25 00:00:00
2022-11-11T12:56:29.477740image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:29.571462image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct110
Distinct (%)0.5%
Missing17318
Missing (%)44.0%
Memory size307.3 KiB
Minimum2017-05-31 00:00:00
Maximum2099-01-01 00:00:00
2022-11-11T12:56:29.679375image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:29.789652image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

length
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct243
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.591936397
Minimum0
Maximum10000
Zeros38334
Zeros (%)97.5%
Negative0
Negative (%)0.0%
Memory size307.3 KiB
2022-11-11T12:56:29.875421image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10000
Range10000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation106.0555133
Coefficient of variation (CV)18.96579392
Kurtosis4414.817351
Mean5.591936397
Median Absolute Deviation (MAD)0
Skewness55.40474886
Sum219886.123
Variance11247.7719
MonotonicityNot monotonic
2022-11-11T12:56:29.997449image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
038334
97.5%
0.572
 
0.2%
3551
 
0.1%
31.550
 
0.1%
4844
 
0.1%
34.538
 
0.1%
6128
 
0.1%
4521
 
0.1%
2220
 
0.1%
2716
 
< 0.1%
Other values (233)648
 
1.6%
ValueCountFrequency (%)
038334
97.5%
0.014
 
< 0.1%
0.0114
 
< 0.1%
0.1541
 
< 0.1%
0.1631
 
< 0.1%
0.1671
 
< 0.1%
0.21
 
< 0.1%
0.2011
 
< 0.1%
0.2221
 
< 0.1%
0.2262
 
< 0.1%
ValueCountFrequency (%)
100002
 
< 0.1%
55001
 
< 0.1%
40001
 
< 0.1%
274010
< 0.1%
26001
 
< 0.1%
25001
 
< 0.1%
22301
 
< 0.1%
21301
 
< 0.1%
20301
 
< 0.1%
18002
 
< 0.1%

width
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct257
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.538041122
Minimum0
Maximum10000
Zeros38235
Zeros (%)97.2%
Negative0
Negative (%)0.0%
Memory size307.3 KiB
2022-11-11T12:56:30.145075image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10000
Range10000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation68.0986718
Coefficient of variation (CV)19.24756368
Kurtosis12023.37956
Mean3.538041122
Median Absolute Deviation (MAD)0
Skewness88.89368975
Sum139122.853
Variance4637.429101
MonotonicityNot monotonic
2022-11-11T12:56:30.313969image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
038235
97.2%
0.571
 
0.2%
2771
 
0.2%
16.558
 
0.1%
15244
 
0.1%
2037
 
0.1%
3036
 
0.1%
16333
 
0.1%
2822
 
0.1%
2518
 
< 0.1%
Other values (247)697
 
1.8%
ValueCountFrequency (%)
038235
97.2%
0.0012
 
< 0.1%
0.0021
 
< 0.1%
0.0042
 
< 0.1%
0.0061
 
< 0.1%
0.0081
 
< 0.1%
0.0110
 
< 0.1%
0.0115
 
< 0.1%
0.0124
 
< 0.1%
0.0141
 
< 0.1%
ValueCountFrequency (%)
100001
 
< 0.1%
30001
 
< 0.1%
18002
 
< 0.1%
152010
< 0.1%
12201
 
< 0.1%
11601
 
< 0.1%
10509
< 0.1%
10102
 
< 0.1%
9901
 
< 0.1%
9705
< 0.1%

height
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct168
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.621937567
Minimum0
Maximum2000
Zeros38279
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size307.3 KiB
2022-11-11T12:56:30.493737image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2000
Range2000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation28.45091296
Coefficient of variation (CV)17.54131204
Kurtosis1384.818326
Mean1.621937567
Median Absolute Deviation (MAD)0
Skewness32.8271002
Sum63777.829
Variance809.4544484
MonotonicityNot monotonic
2022-11-11T12:56:30.629263image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
038279
97.3%
585
 
0.2%
182
 
0.2%
5.7671
 
0.2%
10.548
 
0.1%
6244
 
0.1%
233
 
0.1%
1.533
 
0.1%
1232
 
0.1%
432
 
0.1%
Other values (158)583
 
1.5%
ValueCountFrequency (%)
038279
97.3%
0.0053
 
< 0.1%
0.0064
 
< 0.1%
0.014
 
< 0.1%
0.01514
 
< 0.1%
0.024
 
< 0.1%
0.0511
 
< 0.1%
0.0874
 
< 0.1%
0.11
 
< 0.1%
0.1252
 
< 0.1%
ValueCountFrequency (%)
20001
 
< 0.1%
13301
 
< 0.1%
11302
 
< 0.1%
9508
< 0.1%
9201
 
< 0.1%
8602
 
< 0.1%
8201
 
< 0.1%
8101
 
< 0.1%
8001
 
< 0.1%
76010
< 0.1%

color_code
Categorical

HIGH CARDINALITY
MISSING

Distinct4399
Distinct (%)16.5%
Missing12645
Missing (%)32.2%
Memory size1.9 MiB
10776
 
1756
095A
 
1021
001
 
916
010
 
597
001A
 
427
Other values (4394)
21960 

Length

Max length15
Median length10
Mean length3.611238145
Min length1

Characters and Unicode

Total characters96337
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2507 ?
Unique (%)9.4%

Sample

1st rowAC4H
2nd rowAE2W
3rd row001A
4th rowA433
5th rowBLA

Common Values

ValueCountFrequency (%)
107761756
 
4.5%
095A1021
 
2.6%
001916
 
2.3%
010597
 
1.5%
001A427
 
1.1%
BDS405
 
1.0%
BLA391
 
1.0%
000382
 
1.0%
A0QM381
 
1.0%
01F7372
 
0.9%
Other values (4389)20029
50.9%
(Missing)12645
32.2%

Length

2022-11-11T12:56:30.725329image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
107761756
 
6.6%
095a1021
 
3.8%
001916
 
3.4%
010597
 
2.2%
001a427
 
1.6%
bds405
 
1.5%
bla391
 
1.5%
000382
 
1.4%
a0qm381
 
1.4%
01f7372
 
1.4%
Other values (4389)20031
75.1%

Most occurring characters

ValueCountFrequency (%)
023238
24.1%
111973
12.4%
77078
 
7.3%
A6083
 
6.3%
54868
 
5.1%
24475
 
4.6%
64351
 
4.5%
94198
 
4.4%
34075
 
4.2%
43263
 
3.4%
Other values (27)22735
23.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number70063
72.7%
Uppercase Letter26272
 
27.3%
Space Separator2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A6083
23.2%
B3141
12.0%
D1954
 
7.4%
K1188
 
4.5%
E1138
 
4.3%
S1064
 
4.0%
L1029
 
3.9%
W1000
 
3.8%
M959
 
3.7%
F921
 
3.5%
Other values (16)7795
29.7%
Decimal Number
ValueCountFrequency (%)
023238
33.2%
111973
17.1%
77078
 
10.1%
54868
 
6.9%
24475
 
6.4%
64351
 
6.2%
94198
 
6.0%
34075
 
5.8%
43263
 
4.7%
82544
 
3.6%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common70065
72.7%
Latin26272
 
27.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A6083
23.2%
B3141
12.0%
D1954
 
7.4%
K1188
 
4.5%
E1138
 
4.3%
S1064
 
4.0%
L1029
 
3.9%
W1000
 
3.8%
M959
 
3.7%
F921
 
3.5%
Other values (16)7795
29.7%
Common
ValueCountFrequency (%)
023238
33.2%
111973
17.1%
77078
 
10.1%
54868
 
6.9%
24475
 
6.4%
64351
 
6.2%
94198
 
6.0%
34075
 
5.8%
43263
 
4.7%
82544
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII96337
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
023238
24.1%
111973
12.4%
77078
 
7.3%
A6083
 
6.3%
54868
 
5.1%
24475
 
4.6%
64351
 
4.5%
94198
 
4.4%
34075
 
4.2%
43263
 
3.4%
Other values (27)22735
23.6%

color_label
Categorical

HIGH CARDINALITY

Distinct12907
Distinct (%)32.8%
Missing14
Missing (%)< 0.1%
Memory size2.6 MiB
BLACK
 
2324
NS
 
2039
NOIR
 
693
095A BLACK
 
446
BLANC
 
333
Other values (12902)
33473 

Length

Max length35
Median length27
Mean length13.50208609
Min length1

Characters and Unicode

Total characters530740
Distinct characters66
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8867 ?
Unique (%)22.6%

Sample

1st rowAC4H TRUGR7/TRUGR7/FTWWHT
2nd rowNOIR
3rd rowDEEP MARINE
4th row001A WHITE/BLACK
5th rowDARK PINK-BLACK

Common Values

ValueCountFrequency (%)
BLACK2324
 
5.9%
NS2039
 
5.2%
NOIR693
 
1.8%
095A BLACK446
 
1.1%
BLANC333
 
0.8%
095A BLACK/WHITE307
 
0.8%
WHITE292
 
0.7%
BLA BLACK265
 
0.7%
000 ONECOLOR218
 
0.6%
0019 BLACK196
 
0.5%
Other values (12897)32195
81.9%

Length

2022-11-11T12:56:30.826416image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black7596
 
9.4%
ns2105
 
2.6%
white1814
 
2.2%
blue1568
 
1.9%
noir1146
 
1.4%
1046
 
1.3%
navy1036
 
1.3%
grey1033
 
1.3%
095a1021
 
1.3%
bleu665
 
0.8%
Other values (12864)61944
76.5%

Most occurring characters

ValueCountFrequency (%)
49005
 
9.2%
A40237
 
7.6%
E35689
 
6.7%
L33289
 
6.3%
R26216
 
4.9%
C26027
 
4.9%
B25823
 
4.9%
I22365
 
4.2%
T21708
 
4.1%
N21610
 
4.1%
Other values (56)228771
43.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter402923
75.9%
Decimal Number59722
 
11.3%
Space Separator49015
 
9.2%
Other Punctuation15687
 
3.0%
Dash Punctuation3220
 
0.6%
Connector Punctuation74
 
< 0.1%
Math Symbol29
 
< 0.1%
Open Punctuation29
 
< 0.1%
Close Punctuation29
 
< 0.1%
Lowercase Letter9
 
< 0.1%
Other values (2)3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A40237
 
10.0%
E35689
 
8.9%
L33289
 
8.3%
R26216
 
6.5%
C26027
 
6.5%
B25823
 
6.4%
I22365
 
5.6%
T21708
 
5.4%
N21610
 
5.4%
K19694
 
4.9%
Other values (19)130265
32.3%
Decimal Number
ValueCountFrequency (%)
018549
31.1%
19630
16.1%
55330
 
8.9%
94654
 
7.8%
24325
 
7.2%
34062
 
6.8%
73965
 
6.6%
43435
 
5.8%
63094
 
5.2%
82678
 
4.5%
Other Punctuation
ValueCountFrequency (%)
/15158
96.6%
.341
 
2.2%
,157
 
1.0%
&19
 
0.1%
:5
 
< 0.1%
'4
 
< 0.1%
#1
 
< 0.1%
*1
 
< 0.1%
!1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
p1
11.1%
h1
11.1%
a1
11.1%
l1
11.1%
t1
11.1%
r1
11.1%
e1
11.1%
y1
11.1%
s1
11.1%
Space Separator
ValueCountFrequency (%)
49005
> 99.9%
 10
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-3220
100.0%
Connector Punctuation
ValueCountFrequency (%)
_74
100.0%
Math Symbol
ValueCountFrequency (%)
+29
100.0%
Open Punctuation
ValueCountFrequency (%)
(29
100.0%
Close Punctuation
ValueCountFrequency (%)
)29
100.0%
Currency Symbol
ValueCountFrequency (%)
£2
100.0%
Other Symbol
ValueCountFrequency (%)
©1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin402932
75.9%
Common127808
 
24.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A40237
 
10.0%
E35689
 
8.9%
L33289
 
8.3%
R26216
 
6.5%
C26027
 
6.5%
B25823
 
6.4%
I22365
 
5.6%
T21708
 
5.4%
N21610
 
5.4%
K19694
 
4.9%
Other values (28)130274
32.3%
Common
ValueCountFrequency (%)
49005
38.3%
018549
 
14.5%
/15158
 
11.9%
19630
 
7.5%
55330
 
4.2%
94654
 
3.6%
24325
 
3.4%
34062
 
3.2%
73965
 
3.1%
43435
 
2.7%
Other values (18)9695
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII530677
> 99.9%
None63
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49005
 
9.2%
A40237
 
7.6%
E35689
 
6.7%
L33289
 
6.3%
R26216
 
4.9%
C26027
 
4.9%
B25823
 
4.9%
I22365
 
4.2%
T21708
 
4.1%
N21610
 
4.1%
Other values (50)228708
43.1%
None
ValueCountFrequency (%)
Ç43
68.3%
 10
 
15.9%
É6
 
9.5%
£2
 
3.2%
Ã1
 
1.6%
©1
 
1.6%

inaccurate_gender
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct15
Distinct (%)0.1%
Missing19630
Missing (%)49.9%
Memory size1.7 MiB
HO
6583 
FE
5497 
UN
3759 
GA
1137 
UE
1046 
Other values (10)
1670 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters39384
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFE
2nd rowFE
3rd rowHO
4th rowHO
5th rowUN

Common Values

ValueCountFrequency (%)
HO6583
 
16.7%
FE5497
 
14.0%
UN3759
 
9.6%
GA1137
 
2.9%
UE1046
 
2.7%
UA823
 
2.1%
FI688
 
1.7%
BG64
 
0.2%
UB33
 
0.1%
BF25
 
0.1%
Other values (5)37
 
0.1%
(Missing)19630
49.9%

Length

2022-11-11T12:56:30.910613image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ho6583
33.4%
fe5497
27.9%
un3759
19.1%
ga1137
 
5.8%
ue1046
 
5.3%
ua823
 
4.2%
fi688
 
3.5%
bg64
 
0.3%
ub33
 
0.2%
bf25
 
0.1%
Other values (5)37
 
0.2%

Most occurring characters

ValueCountFrequency (%)
H6583
16.7%
O6583
16.7%
E6569
16.7%
F6210
15.8%
U5673
14.4%
N3779
9.6%
A1960
 
5.0%
G1201
 
3.0%
I688
 
1.7%
B125
 
0.3%
Other values (2)13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter39384
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H6583
16.7%
O6583
16.7%
E6569
16.7%
F6210
15.8%
U5673
14.4%
N3779
9.6%
A1960
 
5.0%
G1201
 
3.0%
I688
 
1.7%
B125
 
0.3%
Other values (2)13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin39384
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
H6583
16.7%
O6583
16.7%
E6569
16.7%
F6210
15.8%
U5673
14.4%
N3779
9.6%
A1960
 
5.0%
G1201
 
3.0%
I688
 
1.7%
B125
 
0.3%
Other values (2)13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII39384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H6583
16.7%
O6583
16.7%
E6569
16.7%
F6210
15.8%
U5673
14.4%
N3779
9.6%
A1960
 
5.0%
G1201
 
3.0%
I688
 
1.7%
B125
 
0.3%
Other values (2)13
 
< 0.1%

country_of_origin
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct74
Distinct (%)0.3%
Missing14402
Missing (%)36.6%
Memory size1.8 MiB
CN
6437 
FR
3465 
VN
1772 
BD
1563 
DK
 
1058
Other values (69)
10625 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters49840
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowCN
2nd rowBD
3rd rowCN
4th rowCN
5th rowNL

Common Values

ValueCountFrequency (%)
CN6437
16.4%
FR3465
 
8.8%
VN1772
 
4.5%
BD1563
 
4.0%
DK1058
 
2.7%
IN1022
 
2.6%
FI952
 
2.4%
TR804
 
2.0%
KH796
 
2.0%
NL633
 
1.6%
Other values (64)6418
16.3%
(Missing)14402
36.6%

Length

2022-11-11T12:56:30.976650image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cn6437
25.8%
fr3465
13.9%
vn1772
 
7.1%
bd1563
 
6.3%
dk1058
 
4.2%
in1022
 
4.1%
fi952
 
3.8%
tr804
 
3.2%
kh796
 
3.2%
nl633
 
2.5%
Other values (64)6418
25.8%

Most occurring characters

ValueCountFrequency (%)
N10310
20.7%
C6725
13.5%
R4441
8.9%
F4417
8.9%
D3553
 
7.1%
T2923
 
5.9%
I2795
 
5.6%
K2665
 
5.3%
B1960
 
3.9%
V1791
 
3.6%
Other values (15)8260
16.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter49840
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N10310
20.7%
C6725
13.5%
R4441
8.9%
F4417
8.9%
D3553
 
7.1%
T2923
 
5.9%
I2795
 
5.6%
K2665
 
5.3%
B1960
 
3.9%
V1791
 
3.6%
Other values (15)8260
16.6%

Most occurring scripts

ValueCountFrequency (%)
Latin49840
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N10310
20.7%
C6725
13.5%
R4441
8.9%
F4417
8.9%
D3553
 
7.1%
T2923
 
5.9%
I2795
 
5.6%
K2665
 
5.3%
B1960
 
3.9%
V1791
 
3.6%
Other values (15)8260
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII49840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N10310
20.7%
C6725
13.5%
R4441
8.9%
F4417
8.9%
D3553
 
7.1%
T2923
 
5.9%
I2795
 
5.6%
K2665
 
5.3%
B1960
 
3.9%
V1791
 
3.6%
Other values (15)8260
16.6%

country_of_manufacture
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct78
Distinct (%)0.3%
Missing14402
Missing (%)36.6%
Memory size1.8 MiB
CN
8632 
FR
2283 
VN
1913 
BD
1868 
IN
1157 
Other values (73)
9067 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters49840
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowCN
2nd rowBD
3rd rowCN
4th rowCN
5th rowTR

Common Values

ValueCountFrequency (%)
CN8632
22.0%
FR2283
 
5.8%
VN1913
 
4.9%
BD1868
 
4.8%
IN1157
 
2.9%
TR1126
 
2.9%
KH800
 
2.0%
DK767
 
2.0%
PK721
 
1.8%
TW519
 
1.3%
Other values (68)5134
 
13.1%
(Missing)14402
36.6%

Length

2022-11-11T12:56:31.042977image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cn8632
34.6%
fr2283
 
9.2%
vn1913
 
7.7%
bd1868
 
7.5%
in1157
 
4.6%
tr1126
 
4.5%
kh800
 
3.2%
dk767
 
3.1%
pk721
 
2.9%
tw519
 
2.1%
Other values (68)5134
20.6%

Most occurring characters

ValueCountFrequency (%)
N12316
24.7%
C8852
17.8%
R3583
 
7.2%
T3284
 
6.6%
D3211
 
6.4%
K2749
 
5.5%
F2297
 
4.6%
I2041
 
4.1%
B2019
 
4.1%
V1936
 
3.9%
Other values (15)7552
15.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter49840
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N12316
24.7%
C8852
17.8%
R3583
 
7.2%
T3284
 
6.6%
D3211
 
6.4%
K2749
 
5.5%
F2297
 
4.6%
I2041
 
4.1%
B2019
 
4.1%
V1936
 
3.9%
Other values (15)7552
15.2%

Most occurring scripts

ValueCountFrequency (%)
Latin49840
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N12316
24.7%
C8852
17.8%
R3583
 
7.2%
T3284
 
6.6%
D3211
 
6.4%
K2749
 
5.5%
F2297
 
4.6%
I2041
 
4.1%
B2019
 
4.1%
V1936
 
3.9%
Other values (15)7552
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII49840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N12316
24.7%
C8852
17.8%
R3583
 
7.2%
T3284
 
6.6%
D3211
 
6.4%
K2749
 
5.5%
F2297
 
4.6%
I2041
 
4.1%
B2019
 
4.1%
V1936
 
3.9%
Other values (15)7552
15.2%

embakment_harbor
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct52
Distinct (%)1.9%
Missing36549
Missing (%)92.9%
Memory size1.3 MiB
GLEIZE
518 
NL
431 
BANGLADESH
250 
CHINE
221 
CN
205 
Other values (47)
1148 

Length

Max length21
Median length11
Mean length5.342589254
Min length2

Characters and Unicode

Total characters14815
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.3%

Sample

1st rowBANGLADESH
2nd rowCHINE
3rd rowGLEIZE
4th rowMM
5th rowCHINE

Common Values

ValueCountFrequency (%)
GLEIZE518
 
1.3%
NL431
 
1.1%
BANGLADESH250
 
0.6%
CHINE221
 
0.6%
CN205
 
0.5%
CHINA157
 
0.4%
ITALIE103
 
0.3%
NETHERLANDS98
 
0.2%
TURKEY75
 
0.2%
FRANCE69
 
0.2%
Other values (42)646
 
1.6%
(Missing)36549
92.9%

Length

2022-11-11T12:56:31.113347image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gleize518
18.5%
nl431
15.4%
bangladesh250
 
8.9%
chine221
 
7.9%
cn205
 
7.3%
china157
 
5.6%
netherlands148
 
5.3%
italie103
 
3.7%
turkey75
 
2.7%
france69
 
2.5%
Other values (46)625
22.3%

Most occurring characters

ValueCountFrequency (%)
E2144
14.5%
N1978
13.4%
I1480
10.0%
L1461
9.9%
A1287
8.7%
G838
 
5.7%
H811
 
5.5%
C673
 
4.5%
Z520
 
3.5%
D478
 
3.2%
Other values (25)3145
21.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter14286
96.4%
Lowercase Letter500
 
3.4%
Space Separator29
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E2144
15.0%
N1978
13.8%
I1480
10.4%
L1461
10.2%
A1287
9.0%
G838
 
5.9%
H811
 
5.7%
C673
 
4.7%
Z520
 
3.6%
D478
 
3.3%
Other values (15)2616
18.3%
Lowercase Letter
ValueCountFrequency (%)
e100
20.0%
h50
10.0%
r50
10.0%
l50
10.0%
a50
10.0%
n50
10.0%
d50
10.0%
s50
10.0%
t50
10.0%
Space Separator
ValueCountFrequency (%)
29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14786
99.8%
Common29
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E2144
14.5%
N1978
13.4%
I1480
10.0%
L1461
9.9%
A1287
8.7%
G838
 
5.7%
H811
 
5.5%
C673
 
4.6%
Z520
 
3.5%
D478
 
3.2%
Other values (24)3116
21.1%
Common
ValueCountFrequency (%)
29
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII14815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E2144
14.5%
N1978
13.4%
I1480
10.0%
L1461
9.9%
A1287
8.7%
G838
 
5.7%
H811
 
5.5%
C673
 
4.5%
Z520
 
3.5%
D478
 
3.2%
Other values (25)3145
21.2%

shipping_date
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct180
Distinct (%)0.8%
Missing15834
Missing (%)40.3%
Infinite0
Infinite (%)0.0%
Mean20198176.2
Minimum20170430
Maximum20210412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size307.3 KiB
2022-11-11T12:56:31.189460image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum20170430
5-th percentile20190801
Q120200102
median20200313
Q320200701
95-th percentile20201015
Maximum20210412
Range39982
Interquartile range (IQR)599

Descriptive statistics

Standard deviation5816.493126
Coefficient of variation (CV)0.0002879712044
Kurtosis2.031064588
Mean20198176.2
Median Absolute Deviation (MAD)388
Skewness-1.17385975
Sum4.744147625 × 1011
Variance33831592.29
MonotonicityNot monotonic
2022-11-11T12:56:31.277010image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202007151286
 
3.3%
202001221161
 
3.0%
20191210903
 
2.3%
20200224842
 
2.1%
20200415769
 
2.0%
20200815621
 
1.6%
20200302618
 
1.6%
20200701614
 
1.6%
20200313608
 
1.5%
20181231518
 
1.3%
Other values (170)15548
39.5%
(Missing)15834
40.3%
ValueCountFrequency (%)
2017043012
 
< 0.1%
2018061572
 
0.2%
201807019
 
< 0.1%
20180901507
1.3%
20181231518
1.3%
2019010110
 
< 0.1%
201905166
 
< 0.1%
201906011
 
< 0.1%
201907012
 
< 0.1%
20190801125
 
0.3%
ValueCountFrequency (%)
202104122
 
< 0.1%
2021040125
 
0.1%
2021032950
 
0.1%
2021030165
 
0.2%
202102244
 
< 0.1%
20210201237
0.6%
2021013024
 
0.1%
20210115174
0.4%
2021011121
 
0.1%
20210101418
1.1%

eco_participation
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.005551599613
Minimum0
Maximum3.15
Zeros38117
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size307.3 KiB
2022-11-11T12:56:31.350930image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3.15
Range3.15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06018643961
Coefficient of variation (CV)10.84127887
Kurtosis640.8368092
Mean0.005551599613
Median Absolute Deviation (MAD)0
Skewness22.62168
Sum218.3
Variance0.003622407513
MonotonicityNot monotonic
2022-11-11T12:56:31.407299image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
038117
96.9%
0.08719
 
1.8%
0.17385
 
1.0%
151
 
0.1%
1.6724
 
0.1%
0.029
 
< 0.1%
0.048
 
< 0.1%
0.065
 
< 0.1%
0.13
 
< 0.1%
3.151
 
< 0.1%
ValueCountFrequency (%)
038117
96.9%
0.029
 
< 0.1%
0.048
 
< 0.1%
0.065
 
< 0.1%
0.08719
 
1.8%
0.13
 
< 0.1%
0.17385
 
1.0%
151
 
0.1%
1.6724
 
0.1%
3.151
 
< 0.1%
ValueCountFrequency (%)
3.151
 
< 0.1%
1.6724
 
0.1%
151
 
0.1%
0.17385
 
1.0%
0.13
 
< 0.1%
0.08719
 
1.8%
0.065
 
< 0.1%
0.048
 
< 0.1%
0.029
 
< 0.1%
038117
96.9%

eco_furniture
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0.0
39321 
3.15
 
1

Length

Max length4
Median length3
Mean length3.000025431
Min length3

Characters and Unicode

Total characters117967
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.039321
> 99.9%
3.151
 
< 0.1%

Length

2022-11-11T12:56:31.474321image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T12:56:31.543819image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
0.039321
> 99.9%
3.151
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
078642
66.7%
.39322
33.3%
31
 
< 0.1%
11
 
< 0.1%
51
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number78645
66.7%
Other Punctuation39322
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
078642
> 99.9%
31
 
< 0.1%
11
 
< 0.1%
51
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.39322
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common117967
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
078642
66.7%
.39322
33.3%
31
 
< 0.1%
11
 
< 0.1%
51
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII117967
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
078642
66.7%
.39322
33.3%
31
 
< 0.1%
11
 
< 0.1%
51
 
< 0.1%

multiple_of_order
Real number (ℝ≥0)

ZEROS

Distinct72
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.78093688
Minimum0
Maximum324
Zeros7440
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size307.3 KiB
2022-11-11T12:56:31.609231image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile10
Maximum324
Range324
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.613947674
Coefficient of variation (CV)3.457089495
Kurtosis110.0964599
Mean2.78093688
Median Absolute Deviation (MAD)0
Skewness8.333001584
Sum109352
Variance92.42798989
MonotonicityNot monotonic
2022-11-11T12:56:31.697901image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127840
70.8%
07440
 
18.9%
101153
 
2.9%
6472
 
1.2%
3430
 
1.1%
12262
 
0.7%
50215
 
0.5%
8189
 
0.5%
60148
 
0.4%
40132
 
0.3%
Other values (62)1041
 
2.6%
ValueCountFrequency (%)
07440
 
18.9%
127840
70.8%
2127
 
0.3%
3430
 
1.1%
410
 
< 0.1%
549
 
0.1%
6472
 
1.2%
8189
 
0.5%
101153
 
2.9%
114
 
< 0.1%
ValueCountFrequency (%)
3241
 
< 0.1%
2621
 
< 0.1%
2102
< 0.1%
1751
 
< 0.1%
1621
 
< 0.1%
1601
 
< 0.1%
1501
 
< 0.1%
1443
< 0.1%
1404
< 0.1%
1321
 
< 0.1%

minimum_multiple_of_order
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct38
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.42505468
Minimum0
Maximum5000
Zeros17829
Zeros (%)45.3%
Negative0
Negative (%)0.0%
Memory size307.3 KiB
2022-11-11T12:56:31.787323image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum5000
Range5000
Interquartile range (IQR)1

Descriptive statistics

Standard deviation211.6869363
Coefficient of variation (CV)20.30559483
Kurtosis535.7023781
Mean10.42505468
Median Absolute Deviation (MAD)1
Skewness23.04697745
Sum409934
Variance44811.35901
MonotonicityNot monotonic
2022-11-11T12:56:31.872332image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
119655
50.0%
017829
45.3%
6556
 
1.4%
3379
 
1.0%
12321
 
0.8%
10175
 
0.4%
8140
 
0.4%
500068
 
0.2%
556
 
0.1%
225
 
0.1%
Other values (28)118
 
0.3%
ValueCountFrequency (%)
017829
45.3%
119655
50.0%
225
 
0.1%
3379
 
1.0%
415
 
< 0.1%
556
 
0.1%
6556
 
1.4%
74
 
< 0.1%
8140
 
0.4%
10175
 
0.4%
ValueCountFrequency (%)
500068
0.2%
30003
 
< 0.1%
25003
 
< 0.1%
15003
 
< 0.1%
100013
 
< 0.1%
5001
 
< 0.1%
2721
 
< 0.1%
2501
 
< 0.1%
2002
 
< 0.1%
1201
 
< 0.1%

net_weight
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct551
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0214366
Minimum0
Maximum12500
Zeros26860
Zeros (%)68.3%
Negative0
Negative (%)0.0%
Memory size307.3 KiB
2022-11-11T12:56:32.352263image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.13
95-th percentile1
Maximum12500
Range12500
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation96.46051026
Coefficient of variation (CV)19.20974373
Kurtosis8707.920133
Mean5.0214366
Median Absolute Deviation (MAD)0
Skewness77.07187952
Sum197452.93
Variance9304.63004
MonotonicityNot monotonic
2022-11-11T12:56:32.432500image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
026860
68.3%
0.5664
 
1.7%
0.3618
 
1.6%
0.35539
 
1.4%
0.1437
 
1.1%
1344
 
0.9%
0.2343
 
0.9%
0.01334
 
0.8%
0.8315
 
0.8%
0.4277
 
0.7%
Other values (541)8591
 
21.8%
ValueCountFrequency (%)
026860
68.3%
0.01334
 
0.8%
0.02100
 
0.3%
0.03211
 
0.5%
0.04226
 
0.6%
0.05220
 
0.6%
0.06166
 
0.4%
0.07185
 
0.5%
0.08183
 
0.5%
0.09211
 
0.5%
ValueCountFrequency (%)
125001
 
< 0.1%
83001
 
< 0.1%
46001
 
< 0.1%
15001
 
< 0.1%
14801
 
< 0.1%
14301
 
< 0.1%
13003
< 0.1%
12801
 
< 0.1%
12101
 
< 0.1%
11901
 
< 0.1%

raw_weight
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct455
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.27072097
Minimum0
Maximum14000
Zeros30149
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size307.3 KiB
2022-11-11T12:56:32.517727image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.8
Maximum14000
Range14000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation98.86412425
Coefficient of variation (CV)43.53864941
Kurtosis13565.35116
Mean2.27072097
Median Absolute Deviation (MAD)0
Skewness108.8920108
Sum89289.29
Variance9774.115063
MonotonicityNot monotonic
2022-11-11T12:56:32.605247image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
030149
76.7%
0.5793
 
2.0%
0.15369
 
0.9%
1298
 
0.8%
0.14226
 
0.6%
0.1213
 
0.5%
0.2198
 
0.5%
0.45188
 
0.5%
0.03186
 
0.5%
0.3177
 
0.5%
Other values (445)6525
 
16.6%
ValueCountFrequency (%)
030149
76.7%
0.0155
 
0.1%
0.0273
 
0.2%
0.03186
 
0.5%
0.04157
 
0.4%
0.05145
 
0.4%
0.06118
 
0.3%
0.07169
 
0.4%
0.08107
 
0.3%
0.09139
 
0.4%
ValueCountFrequency (%)
140001
 
< 0.1%
100001
 
< 0.1%
71001
 
< 0.1%
15001
 
< 0.1%
14801
 
< 0.1%
14301
 
< 0.1%
12801
 
< 0.1%
12101
 
< 0.1%
11901
 
< 0.1%
11703
< 0.1%

volume
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct538
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.545065612
Minimum0
Maximum1010
Zeros34170
Zeros (%)86.9%
Negative0
Negative (%)0.0%
Memory size307.3 KiB
2022-11-11T12:56:32.700982image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.6
Maximum1010
Range1010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22.98586909
Coefficient of variation (CV)14.87695338
Kurtosis1326.062656
Mean1.545065612
Median Absolute Deviation (MAD)0
Skewness34.39569575
Sum60755.07
Variance528.3501776
MonotonicityNot monotonic
2022-11-11T12:56:32.789361image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034170
86.9%
3.6885
 
2.3%
8.84389
 
1.0%
0.3258
 
0.7%
1254
 
0.6%
0.5182
 
0.5%
5.76174
 
0.4%
0.35124
 
0.3%
72114
 
0.3%
96105
 
0.3%
Other values (528)2667
 
6.8%
ValueCountFrequency (%)
034170
86.9%
0.0196
 
0.2%
0.0214
 
< 0.1%
0.0311
 
< 0.1%
0.045
 
< 0.1%
0.0517
 
< 0.1%
0.0612
 
< 0.1%
0.076
 
< 0.1%
0.083
 
< 0.1%
0.0910
 
< 0.1%
ValueCountFrequency (%)
10104
< 0.1%
972.166
< 0.1%
924.461
 
< 0.1%
862.221
 
< 0.1%
859.631
 
< 0.1%
847.953
 
< 0.1%
6449
< 0.1%
6201
 
< 0.1%
494.831
 
< 0.1%
317.521
 
< 0.1%

size
Categorical

HIGH CARDINALITY

Distinct812
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
L
7448 
2XL
5648 
TU
4507 
10
 
1368
36
 
1125
Other values (807)
19226 

Length

Max length13
Median length12
Mean length2.516046997
Min length1

Characters and Unicode

Total characters98936
Distinct characters56
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique288 ?
Unique (%)0.7%

Sample

1st row38.5
2nd row 36
3rd rowU
4th row2XS
5th rowM

Common Values

ValueCountFrequency (%)
L7448
18.9%
2XL5648
 
14.4%
TU4507
 
11.5%
101368
 
3.5%
361125
 
2.9%
3XL1052
 
2.7%
OS883
 
2.2%
34823
 
2.1%
28794
 
2.0%
104600
 
1.5%
Other values (802)15074
38.3%

Length

2022-11-11T12:56:32.868964image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
l7654
19.0%
2xl5648
 
14.0%
tu4507
 
11.2%
101403
 
3.5%
361162
 
2.9%
3xl1052
 
2.6%
os987
 
2.4%
34869
 
2.2%
28811
 
2.0%
35808
 
2.0%
Other values (721)15436
38.3%

Most occurring characters

ValueCountFrequency (%)
L15405
15.6%
211386
11.5%
19189
 
9.3%
38372
 
8.5%
X7744
 
7.8%
05600
 
5.7%
U5027
 
5.1%
T4666
 
4.7%
43446
 
3.5%
53319
 
3.4%
Other values (46)24782
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number48128
48.6%
Uppercase Letter45419
45.9%
Other Punctuation2425
 
2.5%
Space Separator1450
 
1.5%
Dash Punctuation1386
 
1.4%
Lowercase Letter84
 
0.1%
Math Symbol30
 
< 0.1%
Other Number14
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L15405
33.9%
X7744
17.1%
U5027
 
11.1%
T4666
 
10.3%
S2925
 
6.4%
O2007
 
4.4%
E1476
 
3.2%
N1228
 
2.7%
I983
 
2.2%
A856
 
1.9%
Other values (16)3102
 
6.8%
Lowercase Letter
ValueCountFrequency (%)
m30
35.7%
c11
 
13.1%
x9
 
10.7%
i7
 
8.3%
e7
 
8.3%
z5
 
6.0%
n4
 
4.8%
s3
 
3.6%
o2
 
2.4%
r2
 
2.4%
Other values (2)4
 
4.8%
Decimal Number
ValueCountFrequency (%)
211386
23.7%
19189
19.1%
38372
17.4%
05600
11.6%
43446
 
7.2%
53319
 
6.9%
82397
 
5.0%
62349
 
4.9%
91218
 
2.5%
7852
 
1.8%
Other Punctuation
ValueCountFrequency (%)
/1340
55.3%
.1077
44.4%
"6
 
0.2%
'2
 
0.1%
Space Separator
ValueCountFrequency (%)
1450
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1386
100.0%
Math Symbol
ValueCountFrequency (%)
+30
100.0%
Other Number
ValueCountFrequency (%)
½14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common53433
54.0%
Latin45503
46.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L15405
33.9%
X7744
17.0%
U5027
 
11.0%
T4666
 
10.3%
S2925
 
6.4%
O2007
 
4.4%
E1476
 
3.2%
N1228
 
2.7%
I983
 
2.2%
A856
 
1.9%
Other values (28)3186
 
7.0%
Common
ValueCountFrequency (%)
211386
21.3%
19189
17.2%
38372
15.7%
05600
10.5%
43446
 
6.4%
53319
 
6.2%
82397
 
4.5%
62349
 
4.4%
1450
 
2.7%
-1386
 
2.6%
Other values (8)4539
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII98922
> 99.9%
None14
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L15405
15.6%
211386
11.5%
19189
 
9.3%
38372
 
8.5%
X7744
 
7.8%
05600
 
5.7%
U5027
 
5.1%
T4666
 
4.7%
43446
 
3.5%
53319
 
3.4%
Other values (45)24768
25.0%
None
ValueCountFrequency (%)
½14
100.0%

accurate_gender
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
HO
14775 
FE
10372 
UN
4098 
GA
4064 
UA
2677 
Other values (6)
3336 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters78644
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHO
2nd rowFE
3rd rowUA
4th rowFE
5th rowUA

Common Values

ValueCountFrequency (%)
HO14775
37.6%
FE10372
26.4%
UN4098
 
10.4%
GA4064
 
10.3%
UA2677
 
6.8%
FI1798
 
4.6%
UE744
 
1.9%
BG443
 
1.1%
BF246
 
0.6%
ND75
 
0.2%

Length

2022-11-11T12:56:32.935429image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ho14775
37.6%
fe10372
26.4%
un4098
 
10.4%
ga4064
 
10.3%
ua2677
 
6.8%
fi1798
 
4.6%
ue744
 
1.9%
bg443
 
1.1%
bf246
 
0.6%
nd75
 
0.2%

Most occurring characters

ValueCountFrequency (%)
H14775
18.8%
O14775
18.8%
F12416
15.8%
E11116
14.1%
U7549
9.6%
A6741
8.6%
G4507
 
5.7%
N4173
 
5.3%
I1798
 
2.3%
B719
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter78644
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H14775
18.8%
O14775
18.8%
F12416
15.8%
E11116
14.1%
U7549
9.6%
A6741
8.6%
G4507
 
5.7%
N4173
 
5.3%
I1798
 
2.3%
B719
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin78644
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
H14775
18.8%
O14775
18.8%
F12416
15.8%
E11116
14.1%
U7549
9.6%
A6741
8.6%
G4507
 
5.7%
N4173
 
5.3%
I1798
 
2.3%
B719
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII78644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H14775
18.8%
O14775
18.8%
F12416
15.8%
E11116
14.1%
U7549
9.6%
A6741
8.6%
G4507
 
5.7%
N4173
 
5.3%
I1798
 
2.3%
B719
 
0.9%

correct_fedas_1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2
25246 
3
7064 
1
7010 
7
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39322
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
225246
64.2%
37064
 
18.0%
17010
 
17.8%
72
 
< 0.1%

Length

2022-11-11T12:56:33.002874image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T12:56:33.078352image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
225246
64.2%
37064
 
18.0%
17010
 
17.8%
72
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
225246
64.2%
37064
 
18.0%
17010
 
17.8%
72
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number39322
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
225246
64.2%
37064
 
18.0%
17010
 
17.8%
72
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common39322
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
225246
64.2%
37064
 
18.0%
17010
 
17.8%
72
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII39322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
225246
64.2%
37064
 
18.0%
17010
 
17.8%
72
 
< 0.1%

correct_fedas_2
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.42068054
Minimum0
Maximum98
Zeros7602
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size307.3 KiB
2022-11-11T12:56:33.156495image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median46
Q375
95-th percentile78
Maximum98
Range98
Interquartile range (IQR)74

Descriptive statistics

Standard deviation31.8987883
Coefficient of variation (CV)0.7346450564
Kurtosis-1.631273847
Mean43.42068054
Median Absolute Deviation (MAD)29
Skewness-0.2755388946
Sum1707388
Variance1017.532695
MonotonicityNot monotonic
2022-11-11T12:56:33.244981image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
7513111
33.3%
07602
19.3%
643470
 
8.8%
323152
 
8.0%
12594
 
6.6%
782055
 
5.2%
461542
 
3.9%
15897
 
2.3%
16762
 
1.9%
24523
 
1.3%
Other values (34)3614
 
9.2%
ValueCountFrequency (%)
07602
19.3%
12594
 
6.6%
245
 
0.1%
3156
 
0.4%
4157
 
0.4%
514
 
< 0.1%
83
 
< 0.1%
14499
 
1.3%
15897
 
2.3%
16762
 
1.9%
ValueCountFrequency (%)
9811
 
< 0.1%
9052
 
0.1%
8890
 
0.2%
847
 
< 0.1%
8260
 
0.2%
8099
 
0.3%
782055
 
5.2%
7513111
33.3%
702
 
< 0.1%
6931
 
0.1%

correct_fedas_3
Real number (ℝ≥0)

HIGH CORRELATION

Distinct99
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.98471594
Minimum0
Maximum99
Zeros72
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size307.3 KiB
2022-11-11T12:56:33.332670image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q112
median43
Q373
95-th percentile95
Maximum99
Range99
Interquartile range (IQR)61

Descriptive statistics

Standard deviation31.6833359
Coefficient of variation (CV)0.7043133482
Kurtosis-1.313591499
Mean44.98471594
Median Absolute Deviation (MAD)31
Skewness0.2360548141
Sum1768889
Variance1003.833774
MonotonicityNot monotonic
2022-11-11T12:56:33.413769image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124462
 
11.3%
951819
 
4.6%
901369
 
3.5%
11200
 
3.1%
621163
 
3.0%
271095
 
2.8%
511094
 
2.8%
431011
 
2.6%
44965
 
2.5%
36929
 
2.4%
Other values (89)24215
61.6%
ValueCountFrequency (%)
072
 
0.2%
11200
3.1%
2918
2.3%
3705
1.8%
4298
 
0.8%
5549
1.4%
6665
1.7%
7471
 
1.2%
8234
 
0.6%
977
 
0.2%
ValueCountFrequency (%)
99714
 
1.8%
98260
 
0.7%
9713
 
< 0.1%
96502
 
1.3%
951819
4.6%
94248
 
0.6%
93235
 
0.6%
92264
 
0.7%
91212
 
0.5%
901369
3.5%

correct_fedas_4
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.599842327
Minimum0
Maximum9
Zeros1309
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size307.3 KiB
2022-11-11T12:56:33.481254image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.691454749
Coefficient of variation (CV)0.5851189143
Kurtosis-1.265306425
Mean4.599842327
Median Absolute Deviation (MAD)3
Skewness0.002689232808
Sum180875
Variance7.243928665
MonotonicityNot monotonic
2022-11-11T12:56:33.538678image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
76360
16.2%
15807
14.8%
45569
14.2%
24388
11.2%
84153
10.6%
53673
9.3%
32884
7.3%
92727
6.9%
62452
 
6.2%
01309
 
3.3%
ValueCountFrequency (%)
01309
 
3.3%
15807
14.8%
24388
11.2%
32884
7.3%
45569
14.2%
53673
9.3%
62452
 
6.2%
76360
16.2%
84153
10.6%
92727
6.9%
ValueCountFrequency (%)
92727
6.9%
84153
10.6%
76360
16.2%
62452
 
6.2%
53673
9.3%
45569
14.2%
32884
7.3%
24388
11.2%
15807
14.8%
01309
 
3.3%

incorrect_fedas_1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.217410101
Minimum-1
Maximum7
Zeros0
Zeros (%)0.0%
Negative10854
Negative (%)27.6%
Memory size307.3 KiB
2022-11-11T12:56:33.612613image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median2
Q32
95-th percentile3
Maximum7
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.488598113
Coefficient of variation (CV)1.222758142
Kurtosis-0.8573205619
Mean1.217410101
Median Absolute Deviation (MAD)1
Skewness-0.451797563
Sum47871
Variance2.215924342
MonotonicityNot monotonic
2022-11-11T12:56:33.673589image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
217161
43.6%
-110854
27.6%
36183
 
15.7%
14977
 
12.7%
693
 
0.2%
528
 
0.1%
725
 
0.1%
41
 
< 0.1%
ValueCountFrequency (%)
-110854
27.6%
14977
 
12.7%
217161
43.6%
36183
 
15.7%
41
 
< 0.1%
528
 
0.1%
693
 
0.2%
725
 
0.1%
ValueCountFrequency (%)
725
 
0.1%
693
 
0.2%
528
 
0.1%
41
 
< 0.1%
36183
 
15.7%
217161
43.6%
14977
 
12.7%
-110854
27.6%

incorrect_fedas_2
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct52
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.65810488
Minimum-1
Maximum98
Zeros3649
Zeros (%)9.3%
Negative10854
Negative (%)27.6%
Memory size307.3 KiB
2022-11-11T12:56:33.751389image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median32
Q375
95-th percentile78
Maximum98
Range99
Interquartile range (IQR)76

Descriptive statistics

Standard deviation33.5094127
Coefficient of variation (CV)0.9955822772
Kurtosis-1.694404739
Mean33.65810488
Median Absolute Deviation (MAD)33
Skewness0.2307684161
Sum1323504
Variance1122.88074
MonotonicityNot monotonic
2022-11-11T12:56:33.835493image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-110854
27.6%
758201
20.9%
03649
 
9.3%
783508
 
8.9%
323112
 
7.9%
642057
 
5.2%
11268
 
3.2%
461046
 
2.7%
15878
 
2.2%
24737
 
1.9%
Other values (42)4012
 
10.2%
ValueCountFrequency (%)
-110854
27.6%
03649
 
9.3%
11268
 
3.2%
281
 
0.2%
3141
 
0.4%
4373
 
0.9%
514
 
< 0.1%
85
 
< 0.1%
14487
 
1.2%
15878
 
2.2%
ValueCountFrequency (%)
989
 
< 0.1%
903
 
< 0.1%
88157
 
0.4%
8715
 
< 0.1%
8613
 
< 0.1%
847
 
< 0.1%
8232
 
0.1%
816
 
< 0.1%
80100
 
0.3%
783508
8.9%

incorrect_fedas_3
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct100
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.95239306
Minimum-1
Maximum99
Zeros67
Zeros (%)0.2%
Negative10854
Negative (%)27.6%
Memory size307.3 KiB
2022-11-11T12:56:33.919137image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median22
Q351
95-th percentile96
Maximum99
Range100
Interquartile range (IQR)52

Descriptive statistics

Standard deviation33.31714526
Coefficient of variation (CV)1.076399656
Kurtosis-0.6911563711
Mean30.95239306
Median Absolute Deviation (MAD)23
Skewness0.7984227528
Sum1217110
Variance1110.032168
MonotonicityNot monotonic
2022-11-11T12:56:34.002119image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-110854
27.6%
122936
 
7.5%
311605
 
4.1%
901198
 
3.0%
371182
 
3.0%
11174
 
3.0%
271066
 
2.7%
291012
 
2.6%
96984
 
2.5%
99798
 
2.0%
Other values (90)16513
42.0%
ValueCountFrequency (%)
-110854
27.6%
067
 
0.2%
11174
 
3.0%
2136
 
0.3%
3369
 
0.9%
4204
 
0.5%
5360
 
0.9%
6754
 
1.9%
7248
 
0.6%
894
 
0.2%
ValueCountFrequency (%)
99798
2.0%
98672
1.7%
9720
 
0.1%
96984
2.5%
95235
 
0.6%
94291
 
0.7%
93193
 
0.5%
92179
 
0.5%
91260
 
0.7%
901198
3.0%

incorrect_fedas_4
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.789812319
Minimum-1
Maximum9
Zeros987
Zeros (%)2.5%
Negative10854
Negative (%)27.6%
Memory size307.3 KiB
2022-11-11T12:56:34.071022image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median2
Q36
95-th percentile9
Maximum9
Range10
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.304574955
Coefficient of variation (CV)1.184515149
Kurtosis-1.155268062
Mean2.789812319
Median Absolute Deviation (MAD)3
Skewness0.3953028627
Sum109701
Variance10.92021563
MonotonicityNot monotonic
2022-11-11T12:56:34.131077image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
-110854
27.6%
15556
14.1%
73888
 
9.9%
43659
 
9.3%
23365
 
8.6%
32841
 
7.2%
82433
 
6.2%
52172
 
5.5%
92056
 
5.2%
61511
 
3.8%
ValueCountFrequency (%)
-110854
27.6%
0987
 
2.5%
15556
14.1%
23365
 
8.6%
32841
 
7.2%
43659
 
9.3%
52172
 
5.5%
61511
 
3.8%
73888
 
9.9%
82433
 
6.2%
ValueCountFrequency (%)
92056
 
5.2%
82433
6.2%
73888
9.9%
61511
 
3.8%
52172
 
5.5%
43659
9.3%
32841
7.2%
23365
8.6%
15556
14.1%
0987
 
2.5%

Interactions

2022-11-11T12:56:24.993894image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:02.374191image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:03.675840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:05.025073image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:06.719780image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:08.213088image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:09.482357image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:11.033732image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:12.238223image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:13.432930image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:14.681823image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:16.179898image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:17.870623image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:19.252858image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:20.476068image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:21.987848image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:23.608320image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:25.070019image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:02.448207image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:03.739942image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:05.114977image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:06.812697image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:08.286200image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:09.553034image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:11.107870image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:12.305502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:13.501945image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:14.750495image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:16.250889image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:17.961001image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:19.335091image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:20.547364image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:22.057195image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:23.688590image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:25.161264image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:02.519427image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:03.804638image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:05.206242image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:06.914496image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:08.361006image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:09.624078image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:11.177308image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:12.372386image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:13.570973image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:14.818225image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:16.326092image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:18.066177image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:19.414932image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:20.618941image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:22.126383image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:23.767558image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:25.245412image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:02.594041image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:03.873281image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:05.290699image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:07.006582image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:08.441439image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:09.696643image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:11.252125image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:12.443356image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:13.647348image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:14.893276image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:16.409526image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:18.175165image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:19.494266image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:20.695748image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:22.201436image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:23.852483image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:25.344710image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:02.673746image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2022-11-11T12:56:24.790209image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:26.643890image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:03.611233image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:04.763920image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:06.624438image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:08.140651image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:09.410334image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:10.960795image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:12.172700image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:13.364724image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:14.611688image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:16.112423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:17.787160image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:19.178486image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:20.410263image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:21.917317image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:23.529629image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-11-11T12:56:24.875554image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2022-11-11T12:56:34.209454image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-11T12:56:34.352722image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-11T12:56:34.488518image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-11T12:56:34.614868image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-11T12:56:34.718199image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-11T12:56:27.019477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-11T12:56:27.823404image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-11T12:56:28.245234image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-11T12:56:28.471860image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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Last rows

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39312brand_77M9621CCHUCK TAYLOR ALL STARNaN375954CHAUSSUREBASKETSPORTSWEARNaN2021-01-01NaT0.00.00.0600600 REDHOBEIDNaN20200503.00.00.06120.000.000.0035HO375851375954
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39317brand_15257502669IICEPEAK BAUTZENICEPEAK BAUTZENOUTDOOR ADVENTURESHORTNaNNaN2020-06-012020-09-130.00.00.0290290 ANTHRACITEHOFICNNaN20200608.00.00.0110.130.150.0046HO264701-1-1-1-1
39318brand_329CSECURUN12SEMELLES RUN CUSTOMNaN100981RUNNINGUNISEXSEMELLENaN2019-02-012019-12-310.00.00.0NaNNSNaNMAMANaN20190901.00.00.0000.000.000.00LUA14698110981
39319brand_172032B756KATAKANA GRAPHIC TEENaN278135TRAININGFEMMEKATAKANA GRAPHIC TNaN2021-01-152037-12-310.00.00.0002002 PERFORMANCE BLACK/BRILLIANT WHNaNNaNNaNNaNNaN0.00.0100.000.000.00LFE20125278135
39320brand_1FM9969ESSENTIAL TEENaN275124SPORTSTYLEHOMME27-T-SHIRT (SHORT SLEEVE)NaN2020-05-012020-11-300.00.00.0095A095A BLACKNaNNaNNaNNaNNaN0.00.0100.000.000.00LHO20124275124
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